5 papers
SoloQ: Calibration-Free Quantization for Diffusion Language Models
Donghyun Lee, Arkapravo Ghosh, Varun Manjunath +5
Diffusion large language models dLLMs) have emerged as a promising alternative to autoregressive language models through bidirectional diffusion-based token generation. However, th…
BRIM: Workload-Balanced Dual-Sided Bit-Serial Sparse Inference Accelerator
Varun Manjunath, Ruokai Yin, Donghyun Lee +2
Bit-serial accelerators exploit bit-level sparsity to reduce DNN inference cost, but existing designs exploit sparsity on only one operand, bounding the speedup. Extending sparsity…
DiffAxE: Diffusion-driven Hardware Accelerator Generation and Design Space Exploration
Arkapravo Ghosh, Abhishek Moitra, Abhiroop Bhattacharjee +2
Design space exploration (DSE) is critical for developing optimized hardware architectures, especially for AI workloads such as deep neural networks (DNNs) and large language model…
MEADOW: Memory-efficient Dataflow and Data Packing for Low Power Edge LLMs
Abhishek Moitra, Arkapravo Ghosh, Shrey Agarwal +3
The computational and memory challenges of large language models (LLMs) have sparked several optimization approaches towards their efficient implementation. While prior LLM-targete…
Approximate ADCs for In-Memory Computing
Arkapravo Ghosh, Hemkar Reddy Sadana, Mukut Debnath +5
In memory computing (IMC) architectures for deep learning (DL) accelerators leverage energy-efficient and highly parallel matrix vector multiplication (MVM) operations, implemented…